We propose a straightforward vocabulary adaptation scheme to extend the
language capacity of multilingual machine translation models, paving the way
towards efficient continual learning for multilingual machine translation. Our
approach is suitable for large-scale datasets, applies to distant languages
with unseen scripts, incurs only minor degradation on the translation
performance for the original language pairs and provides competitive
performance even in the case where we only possess monolingual data for the new
languages.

本研究提出了一种简单的词汇适应方案，以扩大多语言机器翻译模型的语言容量，为多语言机器翻译的高效连续学习铺平了道路。我们的方法适用于大规模数据集，适用于未见过脚本的远程语言，在原始语言对的翻译性能上只有轻微的降低，在仅拥有新语言的单语数据的情况下也提供了具有竞争力的性能。

通过词汇替换实现多语言机器翻译的连续学习

Towards Continual Learning for Multilingual Machine Translation via  Vocabulary Substitution

Generative adversarial networks (GANs) have succeeded in inducing
cross-lingual word embeddings -- maps of matching words across languages --
without supervision. Despite these successes, GANs' performance for the
difficult case of distant languages is still not satisfactory. These
limitations have been explained by GANs' incorrect assumption that source and
target embedding spaces are related by a single linear mapping and are
approximately isomorphic. We assume instead that, especially across distant
languages, the mapping is only piece-wise linear, and propose a
multi-adversarial learning method. This novel method induces the seed
cross-lingual dictionary through multiple mappings, each induced to fit the
mapping for one subspace. Our experiments on unsupervised bilingual lexicon
induction show that this method improves performance over previous
single-mapping methods, especially for distant languages.

提出了一种基于多对抗学习的新方法，用于在远程语言之间诱导跨语言词嵌入，改善了以往单映射方法的性能，特别是对于远程语言的情况

多重对抗学习用于跨语言单词嵌入

Multi-Adversarial Learning for Cross-Lingual Word Embeddings

Unsupervised neural machine translation (NMT) has attracted a lot of
attention recently. While state-of-the-art methods for unsupervised translation
usually perform well between similar languages (e.g., English-German
translation), they perform poorly between distant languages, because
unsupervised alignment does not work well for distant languages. In this work,
we introduce unsupervised pivot translation for distant languages, which
translates a language to a distant language through multiple hops, and the
unsupervised translation on each hop is relatively easier than the original
direct translation. We propose a learning to route (LTR) method to choose the
translation path between the source and target languages. LTR is trained on
language pairs whose best translation path is available and is applied on the
unseen language pairs for path selection. Experiments on 20 languages and 294
distant language pairs demonstrate the advantages of the unsupervised pivot
translation for distant languages, as well as the effectiveness of the proposed
LTR for path selection. Specifically, in the best case, LTR achieves an
improvement of 5.58 BLEU points over the conventional direct unsupervised
method.

本研究提出了一种基于无监督的跨语言翻译模型，采用多跳的方式将源语言翻译为目标语言，通过语言对之间的最佳翻译路径选择，最终在 20 种语言和 294 个跨度较大的语言对的实验中展示了该方法的优势。